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Update app.py
Browse filesTaiwan_Tax_Knowledge-base
app.py
CHANGED
@@ -10,7 +10,7 @@ from langchain_groq import ChatGroq
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_huggingface import HuggingFaceEmbeddings
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from langchain_community.vectorstores import Chroma
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from
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from langchain.chains import RetrievalQA
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from langchain_community.document_loaders import WebBaseLoader, TextLoader
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from langchain.prompts import PromptTemplate
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@@ -74,18 +74,19 @@ text_splitter = RecursiveCharacterTextSplitter(
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)
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split_docs = text_splitter.split_documents(documents)
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print(f"
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embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-large-zh-v1.5")
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print(f"\n成功初始化嵌入模型")
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vectorstore = Chroma.from_documents(split_docs, embeddings, persist_directory="./Knowledge-base")
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print(f"成功建立 Chroma 向量資料庫")
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retriever = vectorstore.as_retriever(search_kwargs={"k": 2})
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template = """Let's work this out in a step by step way to be sure we have the right answer. Must reply to me in Taiwanese Traditional Chinese.
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-
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如果檢索到的多個上下文之間存在聯繫,請整合這些訊息以提供全面的回答,但要避免過度推斷。
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如果檢索到的上下文不包含足夠回答問題的訊息,請誠實的說明,不要試圖編造答案。
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain_huggingface import HuggingFaceEmbeddings
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from langchain_community.vectorstores import Chroma
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from langchain_community.embeddings import HuggingFaceEmbeddings
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from langchain.chains import RetrievalQA
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from langchain_community.document_loaders import WebBaseLoader, TextLoader
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from langchain.prompts import PromptTemplate
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)
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split_docs = text_splitter.split_documents(documents)
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print(f"分割後的文件數量:{len(split_docs)}")
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embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-large-zh-v1.5")
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print(f"\n成功初始化嵌入模型")
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print(f"開始建立向量資料庫")
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vectorstore = Chroma.from_documents(split_docs, embeddings, persist_directory="./Knowledge-base")
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print(f"成功建立 Chroma 向量資料庫")
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retriever = vectorstore.as_retriever(search_kwargs={"k": 2})
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template = """Let's work this out in a step by step way to be sure we have the right answer. Must reply to me in Taiwanese Traditional Chinese.
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在回答之前,請仔細分析檢索到的上下文,確保你的回答準確完整反映了上下文中的訊息,而不是依賴先前的知識,在回應的答案中不要提到是根據上下文回答。
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如果檢索到的多個上下文之間存在聯繫,請整合這些訊息以提供全面的回答,但要避免過度推斷。
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如果檢索到的上下文不包含足夠回答問題的訊息,請誠實的說明,不要試圖編造答案。
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